

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__snake_case				:      Tuple   		=			{
    """configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      str   		=			[
	    """MEGA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MegaForCausalLM""",
	    """MegaForMaskedLM""",
	    """MegaForMultipleChoice""",
	    """MegaForQuestionAnswering""",
	    """MegaForSequenceClassification""",
	    """MegaForTokenClassification""",
	    """MegaModel""",
	    """MegaPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mega import (
		    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MegaForCausalLM,
		    MegaForMaskedLM,
		    MegaForMultipleChoice,
		    MegaForQuestionAnswering,
		    MegaForSequenceClassification,
		    MegaForTokenClassification,
		    MegaModel,
		    MegaPreTrainedModel,
		)

else:
	import sys

	__snake_case				:      Optional[int]   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





from random import randint
from tempfile import TemporaryFile

import numpy as np


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						List[Any] 							= 0
			if start < end:
						a_						:						Dict 							= randint(a__  ,	a__)
						a_						:						List[str] 							= a[end]
						a_						:						Tuple 							= a[pivot]
						a_						:						Tuple 							= temp

						a_							,					a_						:						List[Any] 							= _in_place_partition(a__  ,	a__  ,	a__)
						count += _in_place_quick_sort(a__  ,	a__  ,	p - 1)
						count += _in_place_quick_sort(a__  ,	p + 1  ,	a__)
			return count


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= 0
			a_						:						Union[str, Any] 							= randint(a__  ,	a__)
			a_						:						Union[str, Any] 							= a[end]
			a_						:						Any 							= a[pivot]
			a_						:						Any 							= temp
			a_						:						int 							= start - 1
			for index in range(a__  ,	a__):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									a_						:						str 							= new_pivot_index + 1
									a_						:						Optional[Any] 							= a[new_pivot_index]
									a_						:						str 							= a[index]
									a_						:						Union[str, Any] 							= temp

			a_						:						Union[str, Any] 							= a[new_pivot_index + 1]
			a_						:						Tuple 							= a[end]
			a_						:						Any 							= temp
			return new_pivot_index + 1, count


__snake_case				:      Union[str, Any]   		=			TemporaryFile()
__snake_case				:      Dict   		=			1_00  # 1000 elements are to be sorted


__snake_case		, __snake_case				:      int   		=			0, 1  # mean and standard deviation
__snake_case				:      Tuple   		=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
__snake_case				:      str   		=			np.load(outfile)
__snake_case				:      Dict   		=			len(M) - 1
__snake_case				:      Dict   		=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)



print(z)


